主要总结了论文相关代码
物理驱动深度学习代码
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations 用内嵌物理信息的神经网络求解PDE的源头文章，从数据驱动角度提出PINN，求解PDE正逆问题。代码链接。
- DGM, (DGM: A deep learning algorithm for solving partial differential equations): A deep learning algorithm for solving partial differential equations：对于高维PDE用数值方法不能求解，提出DGM方法即用神经网络求解高维PDE问题，同时提出用Monte Carlo估计方法代替PDE中的二阶导数能够加速学习。代码链接。
- hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition：采用变分区域的方式对区域内采点，这是一种启发式的踩点方法，在准确性上有所提升。代码链接。
- Temperature field inversion of heat-source systems via physics-informed neural network：提出了基于内嵌物理知识神经网络的温度场重构算法。该方法能够减少温度场重构对测点数量的依赖，利用少量测点准确重构温度场。同时改论文提出了一种基于条件数的测点选择方法，能够提升重构模型的鲁棒性。代码链接。
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations。代码链接。
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain：代码链接，python。
- Physics-Informed Neural Networks for Power Systems：代码链接，python。
- Physics-informed neural network (PINN) for solving fluid dynamics problems：代码链接。
- Adversarial Uncertainty Quantification in Physics-Informed Neural Networks：代码链接。
- Accelerating Training PINN with Prior Dictionary：从网络角度引入先验字典对PINN进行改进，加速训练。代码链接。